Describing crmPack objects with knit_print in Rmd/qmd documents

Utilities
This vignette describes how crmPack objects are rendered in a user friendly manner in Rmd and qmd documents using knit_print methods.
Author

John Kirkpatrick, Daniel Sabanés Bové

Published

February 13, 2026

Introduction

Objects created by crmPack are almost always S4 objects. Like all S4 objects, by default they do not render in a particularly user-friendly way.

cs <- CohortSizeDLT(intervals = 0:2, cohort_size = c(1, 3, 5))
cs
#> An object of class "CohortSizeDLT"
#> Slot "intervals":
#> [1] 0 1 2
#> 
#> Slot "cohort_size":
#> [1] 1 3 5

Fortunately, a little known feature of knitr can put this right at little or no cost to the end user: in the simplest case, demonstrated below, all that needs to be done is to reference the object in a markdown or Quarto chunk.

cs
Defined by the number of toxicities so far observed
No of toxicities
Lower Upper Cohort size
0 1 1
1 2 3
2 Inf 5

The knit_printmethods provided by crmPack are not intended to be fully customisable or comprehensive. We do believe, however, that they cover the vast majority of use-cases and are easily extended using the techniques described later in this vignette.

Formatting of these objects currently only works for HMTL output. If another format - such as PDF or Microsoft Word - is required, our suggested workaround is to create the HTML output and then print or save the document to the required format.

How is this done?

When running code at the console, the result of an R function or statement that is not assigned to an object is printed. (Unless, of course, it is returned invisiblely.) The same process appears to happen when the chunks of a markdown or Quarto document are evaluated. But that is not quite the case. Instead, the result is passed to an S3 function called knit_print (Xie 2024). It is the results of running knit_print on the returned expression that appear in the rendered document.

As a simple demonstration of the concept, consider:

knit_print.DustySpringfield <- function(x, ...) {
  "I just don't know what to do with myself"
}

lyric <- 10

lyric
#> [1] 10

class(lyric) <- "DustySpringfield"

lyric
#> I just don't know what to do with myself

The actions of knit_print are entirely arbitrary, but this mechanism provides developers with an easy way to provide nicely-rendered versions of any objects that are rendered by knitr. We have provided such methods for (almost) all crmPack classes.

Using knit_print in crmPack

By default, all that needs to be done is to reference the object to be printed in a markdown or quarto chunk. This is equivalent to knit_print(object). However, the knit_print methods for most crmPack classes have optional arguments that can be used to customise the way in which the object is rendered. To change the default value of any parameter to knit_print the function must be called explicitly: knit_print(cs, tox_label = "DLAE").

Common customisations

The most commonly needed customisations are to alter the way in which participants and toxicities are described. These are handled by the label and tox_label arguments to knit_print.

These arguments can be provided either as a scalar or a vector of length 2. If a vector, the first element is taken to describe a single instance and the second any other number of instances. If a scalar, it is converted to a vector, whose first element is the scalar value provided and the second the scalar with "s" appended1.

So, for example:

CohortSizeConst(3)

A constant size of 3 participants.

knit_print(CohortSizeConst(3), label = "subject")

A constant size of 3 subjects.

Dose units are defined by the units parameter. By default, no units are printed.

d <- Data(doseGrid = c(0.1, 0.3, 0.9, 2.5, 5, 10, 15))
d

No participants are yet evaluable.

The dose grid is 0.1, 0.3, 0.9, 2.5, 5, 10 and 15.

knit_print(d, units = "mg/dL")

No participants are yet evaluable.

The dose grid is 0.1 mg/dL, 0.3 mg/dL, 0.9 mg/dL, 2.5 mg/dL, 5 mg/dL, 10 mg/dL and 15 mg/dL.

The format used to display dose levels (and other information in other classes) can be changed with the fmt parameter:

knit_print(d, units = "mg/dL", fmt = "%.2f")

No participants are yet evaluable.

The dose grid is 0.10 mg/dL, 0.30 mg/dL, 0.90 mg/dL, 2.50 mg/dL, 5.00 mg/dL, 10.00 mg/dL and 15.00 mg/dL.

biomarker_label and biomarker_units allow the representation of a biomarker to be customised.

x <- .DefaultDualEndpointRW()
x

The relationships between dose and toxicity and between dose and PD biomarker will be modelled simultaneously.

A probit log normal model will describe the relationship between dose and toxicity: \[ \Phi^{-1}(Tox | d) = f(X = 1 | \theta, d) = \alpha + \beta \cdot log(d/d^*) \]where d* denotes a reference dose.

The prior for θ is given by\[ \boldsymbol\theta = \begin{bmatrix}\alpha \\ \beta\end{bmatrix}\sim N \left(\begin{bmatrix} 0.00 \\ 1.00\end{bmatrix} , \begin{bmatrix} 1.00 & 0.00 \\ 0.00 & 1.00\end{bmatrix} \right) \]

The reference dose will be 1.00.

The PD biomarker response w at dose d is modelled as \[ w(d) \sim N(f(d), \sigma_w^2) \]

where f(d) is a first order random walk such that

\[ f(d) = \beta_{W_i} - \beta_{W_{i - 1}}\sim N(0, 0.01 \times (d_i - d_{i - 1})) \]


knit_print(x, biomarker_name = "CRP", biomarker_units = "mg/dL")

The relationships between dose and toxicity and between dose and PD biomarker will be modelled simultaneously.

A probit log normal model will describe the relationship between dose and toxicity: \[ \Phi^{-1}(Tox | d) = f(X = 1 | \theta, d) = \alpha + \beta \cdot log(d/d^*) \]where d* denotes a reference dose.

The prior for θ is given by\[ \boldsymbol\theta = \begin{bmatrix}\alpha \\ \beta\end{bmatrix}\sim N \left(\begin{bmatrix} 0.00 \\ 1.00\end{bmatrix} , \begin{bmatrix} 1.00 & 0.00 \\ 0.00 & 1.00\end{bmatrix} \right) \]

The reference dose will be 1.00.

The PD biomarker response w at dose d is modelled as \[ w(d) \sim N(f(d), \sigma_w^2) \]

where f(d) is a first order random walk such that

\[ f(d) = \beta_{W_i} - \beta_{W_{i - 1}}\sim N(0, 0.01 \times (d_i - d_{i - 1})) \]

Rendering complex classes

Some crmPack classes have slots whose values are themselves crmPack classes. CohortSizeMax is a simple example. In these cases, the slot values are each passed to knit_print in turn.

.DefaultCohortSizeMax()
The maximum of the cohort sizes defined in the following rules:
Defined by the dose to be used in the next cohort
Dose
Lower Upper Cohort size
0 10 1
10 Inf 3
Defined by the number of toxicities so far observed
No of toxicities
Lower Upper Cohort size
0 1 1
1 Inf 3

knit_print methods for sub-classes of RuleDesign (and related classes) offer slightly more control. Here, an overall header for the rendition of the object is provided by the title parameter (whose value defaults to “Design” and the slot values are separated by sub-headers. The styling of the overall header and sub-headers is controlled by the level parameter. The default value of level is 2L, and the level of slots is defined recursively to be one more than the level of the parent slot2. Class-specific parameters are passed to slot-specific knit_print methods using ....

knit_print(.DefaultDesign())

Design

Dose toxicity model

A logistic log normal model will describe the relationship between dose and toxicity: \[ p(Tox | d) = f(X = 1 | \theta, d) = \frac{e^{\alpha + \beta \cdot log(d/d_{ref})}}{1 + e^{\alpha + \beta \cdot log(d/d_{ref})}} \]where dref denotes a reference dose.

The prior for θ is given by\[ \boldsymbol\theta = \begin{bmatrix}\alpha \\ log(\beta)\end{bmatrix}\sim N \left(\begin{bmatrix}-0.85 \\ 1.00\end{bmatrix} , \begin{bmatrix} 1.00 & -0.50 \\ -0.50 & 1.00\end{bmatrix} \right) \]

The reference dose will be 56.00.

Stopping rule

If either of the following rules are TRUE:

  • If both of the following rules are TRUE:

    • ≥ 3 cohorts dosed: If 3 or more cohorts have been treated.

    • P(0.2 ≤ prob(DLE | NBD) ≤ 0.35) ≥ 0.5: If the probability of toxicity at the next best dose is in the range [0.20, 0.35] is at least 0.50.

  • ≥ 20 patients dosed: If 20 or more participants have been treated.

Escalation rule

Defined by highest dose administered so far
Dose
Min Max Increment
0 20 1.00
20 Inf 0.33

Use of placebo

Placebo will not be administered in the trial.

Backfill cohorts

No backfill cohorts at all will be opened.

Dose recommendation

The dose recommended for the next cohort will be chosen in the following way. First, doses that are ineligible according to the increments rule will be discarded. Next, any dose for which the mean posterior probability of toxicity being in the overdose range - (0.35, 1] - is 0.25 or more will also be discarded. Finally, the dose amongst those remaining which has the highest chance that the mean posterior probability of toxicity is in the target toxicity range of 0.2 to 0.35 (inclusive) will be selected.

Cohort size

The maximum of the cohort sizes defined in the following rules:
Defined by the dose to be used in the next cohort
Dose
Lower Upper Cohort size
0 30 1
30 Inf 3
Defined by the number of toxicities so far observed
No of toxicities
Lower Upper Cohort size
0 1 1
1 Inf 3

Observed data

No participants are yet evaluable.

The dose grid is 1, 3, 5, 10, 15, 20, 25, 40, 50, 80 and 100.

Starting dose

The starting dose is 3.

Slot headers can be customised using the sections parameter. sections should be a named vector. Names should be valid slot names for the object being rendered and values the requested slot headers.

knit_print(
  .DefaultDesign(),
  level = 4,
  sections = c(
    "nextBest" = "Selection of the dose for the following cohort",
    "startingDose" = "Initial dose"
  )
)

We omit here the output for brevity.

It is not possible to omit slots from the rendition of a crmPack object. If you need to do this, you can either render the required slots individually, or override the definition of knit_print for the super class as demonstrated below.

Restoring console-like behaviour

To restore the default behaviour for crmPack objects, simply wrap the object in a call to normal_print().

normal_print(cs)
#> An object of class "CohortSizeDLT"
#> Slot "intervals":
#> [1] 0 1 2
#> 
#> Slot "cohort_size":
#> [1] 1 3 5

Accessing the output of knit_print

One of the parameters of knitr::knit_print is asis, with a default value of TRUE. asis has the same effect as setting the chunk option output to asis. This is achieved by returning an object of class knit-asis.

Setting asis to FALSE will display the raw HTML code generated by knit_print to be displayed. Alternatively, it may allow easier manipulation of the return value.

csOutput1 <- knit_print(CohortSizeDLT(intervals = 0:2, cohort_size = c(1, 3, 5)))
class(csOutput1)
#> [1] "knit_asis"
csOutput1
Defined by the number of toxicities so far observed
No of toxicities
Lower Upper Cohort size
0 1 1
1 2 3
2 Inf 5 
csOutput2 <- knit_print(CohortSizeDLT(intervals = 0:2, cohort_size = c(1, 3, 5)), asis = FALSE)
class(csOutput2)
#> [1] "character"
csOutput2
#> [1] "<table>\n<caption>Defined by the number of toxicities so far observed</caption>\n <thead>\n<tr>\n<th style=\"border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; \" colspan=\"2\"><div style=\"border-bottom: 1px solid #ddd; padding-bottom: 5px; \">No of toxicities</div></th>\n<th style=\"empty-cells: hide;border-bottom:hidden;\" colspan=\"1\"></th>\n</tr>\n  <tr>\n   <th style=\"text-align:right;\"> Lower </th>\n   <th style=\"text-align:right;\"> Upper </th>\n   <th style=\"text-align:right;\"> Cohort size </th>\n  </tr>\n </thead>\n<tbody>\n  <tr>\n   <td style=\"text-align:right;\"> 0 </td>\n   <td style=\"text-align:right;\"> 1 </td>\n   <td style=\"text-align:right;\"> 1 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 1 </td>\n   <td style=\"text-align:right;\"> 2 </td>\n   <td style=\"text-align:right;\"> 3 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 2 </td>\n   <td style=\"text-align:right;\"> Inf </td>\n   <td style=\"text-align:right;\"> 5 </td>\n  </tr>\n</tbody>\n</table>\n\n"

But with the chunk option output set to asis…

cat(csOutput2)
Defined by the number of toxicities so far observed
No of toxicities
Lower Upper Cohort size
0 1 1
1 2 3
2 Inf 5

Providing your own knit_print method

If the methods provided in crmPack don’t do what you want, it’s easy to roll your own, using standard S3 techniques.

The formal arguments to knitr::knit_print are x and .... Additional arguments can be added after ....

As an example, consider knit_print.NextBestNCRM, which currently returns a paragraph of text:

.DefaultNextBestNCRM()

The dose recommended for the next cohort will be chosen in the following way. First, doses that are ineligible according to the increments rule will be discarded. Next, any dose for which the mean posterior probability of toxicity being in the overdose range - (0.35, 1] - is 0.25 or more will also be discarded. Finally, the dose amongst those remaining which has the highest chance that the mean posterior probability of toxicity is in the target toxicity range of 0.2 to 0.35 (inclusive) will be selected.

You might feel this is better presented as a bulleted list. You can achieve this as follows3:

knit_print.NextBestNCRM <- function(x, ...) {
  knitr::asis_output(
    paste0(
      "The dose recommended for the next cohort will be chosen in the following ",
      "way.\n\n-  First, doses that are ineligible according to the increments rule ",
      "will be discarded.\n-  Next, any dose for which the mean posterior probability of ",
      " toxicity being in the overdose range - (",
      x@overdose[1], ", ", x@overdose[2],
      "] - is ",
      x@max_overdose_prob,
      " or more will also be discarded.\n-  Finally, the dose amongst those remaining ",
      "which has the highest chance that the mean posterior probability of toxicity ",
      "is in the target toxicity range of ",
      x@target[1],
      " to ",
      x@target[2],
      " (inclusive) will be selected.\n\n"
    )
  )
}
registerS3method("knit_print", "NextBestNCRM", knit_print.NextBestNCRM)

.DefaultNextBestNCRM()

The dose recommended for the next cohort will be chosen in the following way.

  • First, doses that are ineligible according to the increments rule will be discarded.
  • Next, any dose for which the mean posterior probability of toxicity being in the overdose range - (0.35, 1] - is 0.25 or more will also be discarded.
  • Finally, the dose amongst those remaining which has the highest chance that the mean posterior probability of toxicity is in the target toxicity range of 0.2 to 0.35 (inclusive) will be selected.

Class coverage

crmPack defines 138 classes. Custom knit_print methods exist for 102 of them. Of the remaining 36 classes, 25 are virtual classes that will never be directly instantiated by end users. That leaves 11 classes for which knit_print methods may be useful. These classes are listed below.

Class
DualSimulationsSummary
NextBestEWOC
Simulations
StoppingExternal
DualSimulations
GeneralSimulations
Samples
DASimulations
IncrementsMaxToxProb
EffFlexi
McmcOptions

The majority of these classes relate to simulation of the operating characteristics of CRM trials. Reporting of this information is likely to need customisation that is beyond the scope of a simple function4.

References

Xie, Yihui. 2024. “Custom Print Methods.” https://CRAN.R-project.org/package=knitr/vignettes/knit_print.html.

Footnotes

  1. Except for tox_label = "toxicity", which becomes tox_label = c("toxicity", "toxicities").↩︎

  2. Because markdown header styles are defined only for six levels, the greatest value for level, including values generated by nested calls, is 6.↩︎

  3. For simplicity, the tox_label and asis parameters, which are defined in the current implementation of the function, are omitted in this custom implementation. They should be preserved in any “real world” customisation.↩︎

  4. The crmPack team is considering the creation of markdown or Quarto templates that may assist in this area, but consider this to be a long-term ambition.↩︎

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